Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.getmuster.io/llms.txt

Use this file to discover all available pages before exploring further.

​
How it works

The LangChain connector monitors your agents in two modes: LangSmith mode (zero code changes): If your team is already tracing to LangSmith, the connector polls the LangSmith API for completed runs. No changes to your agent code. Callback mode (real-time): Your team adds MusterCallbackHandler to your chain config. Executions are sent to muster in real time as they complete. Elitery deploys and manages the connector. Your developers choose which mode suits them.
If you’re already sending traces to LangSmith:
  1. Provide Elitery with your LangSmith API key during onboarding
  2. Elitery deploys the connector — it immediately starts discovering agents and streaming executions
  3. No changes to your agent code
The connector polls LangSmith every 30 seconds for new completed runs. Required credentials for Elitery: 
LANGSMITH_API_KEY=ls__...
LANGSMITH_PROJECT=your-project-name   # optional, defaults to all projects

​
Option B — Callback mode (real-time, one line of code)

If you’re not using LangSmith, or want real-time data without the 30-second polling delay: 
from muster.callback import MusterCallbackHandler

# Add the callback to your chain or agent
result = chain.invoke(
    {"input": user_input},
    config={"callbacks": [MusterCallbackHandler()]}
)
Set these env vars in your agent’s environment: 
MUSTER_BACKEND_URL=https://api.yourcompany.getmuster.io
MUSTER_CONNECTOR_ID=<your-connector-id>   # provided by Elitery
The callback captures:
  • Input and output data
  • Token usage (prompt + completion tokens)
  • Model name
  • Execution status (success/error)
  • Start and end timestamps
It fires and forgets — never blocks your chain execution.

​
What muster does automatically

Once executions arrive (via either mode), muster’s check inference engine:
  1. Matches your agent name to a check category (financial, legal, risk, etc.)
  2. Inspects output field names for additional check suggestions
  3. Emits a quality signal with inferred pass/fail checks
  4. Surfaces results in your Health Heatmap and Executive dashboard

​
Adding precise checks with the SDK

For agents where business rules matter (arithmetic validation, required fields, decision enums), add the SDK alongside the connector: 
import httpx, threading

def muster_emit(job_id: str, checks: list):
    def _send():
        try:
            httpx.post(
                f"{MUSTER_BACKEND_URL}/api/v1/jobs/{job_id}/quality",
                json={"agent_id": "contract-review-agent", "checks": checks},
                timeout=2.0,
            )
        except Exception:
            pass
    threading.Thread(target=_send, daemon=True).start()

# After your chain runs:
muster_emit(job_id, checks=[
    {"check_id": "output_not_empty",  "severity": "HIGH", "passed": bool(result)},
    {"check_id": "risk_flag_present", "severity": "HIGH", "passed": "risk_flags" in result},
    {"check_id": "source_cited",      "severity": "HIGH", "passed": "source" in result},
])
See the Signal integration guide → for full details.